"""simple docstring"""


import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   __UpperCAmelCase		(      __lowerCAmelCase , unittest.TestCase      ):
				A__ :  Any    				=			DDIMPipeline
				A__ :  Dict    				=			UNCONDITIONAL_IMAGE_GENERATION_PARAMS
				A__ :  str    				=			PipelineTesterMixin.required_optional_params - {
				    '''num_images_per_prompt''',
				    '''latents''',
				    '''callback''',
				    '''callback_steps''',
				}
				A__ :  Any    				=			UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
				A__ :  List[str]    				=			False

				def 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =UNetaDModel(
										    block_out_channels=(32, 64)      ,					layers_per_block=2      ,					sample_size=32      ,					in_channels=3      ,					out_channels=3      ,					down_block_types=("DownBlock2D", "AttnDownBlock2D")      ,					up_block_types=("AttnUpBlock2D", "UpBlock2D")      ,					)
										lowerCamelCase__   =DDIMScheduler()
										lowerCamelCase__   ={"unet": unet, "scheduler": scheduler}
										return components

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase=0 ):







										if str(lowercase_ ).startswith("mps" ):
																lowerCamelCase__   =torch.manual_seed(lowercase_ )
										else:
																lowerCamelCase__   =torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )
										lowerCamelCase__   ={
										    "batch_size": 1,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}
										return inputs

				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"

										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =self.pipeline_class(**lowercase_ )
										pipe.to(lowercase_ )
										pipe.set_progress_bar_config(disable=lowercase_ )

										lowerCamelCase__   =self.get_dummy_inputs(lowercase_ )
										lowerCamelCase__   =pipe(**lowercase_ ).images
										lowerCamelCase__   =image[0, -3:, -3:, -1]

										self.assertEqual(image.shape      ,					(1, 32, 32, 3) )
										lowerCamelCase__   =np.array(
										    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
										lowerCamelCase__   =np.abs(image_slice.flatten() - expected_slice ).max()
										self.assertLessEqual(lowercase_      ,					1E-3 )

				def 						_a      ( self ):
										super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )

				def 						_a      ( self ):
										super().test_save_load_local(expected_max_difference=3E-3 )

				def 						_a      ( self ):
										super().test_save_load_optional_components(expected_max_difference=3E-3 )






				def 						_a      ( self ):
										super().test_inference_batch_single_identical(expected_max_diff=3E-3 )






@slow
@require_torch_gpu
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										lowerCamelCase__   ="google/ddpm-cifar10-32"

										lowerCamelCase__   =UNetaDModel.from_pretrained(lowercase_ )
										lowerCamelCase__   =DDIMScheduler()

										lowerCamelCase__   =DDIMPipeline(unet=lowercase_      ,					scheduler=lowercase_ )
										ddim.to(lowercase_ )
										ddim.set_progress_bar_config(disable=lowercase_ )

										lowerCamelCase__   =torch.manual_seed(0 )
										lowerCamelCase__   =ddim(generator=lowercase_      ,					eta=0.0      ,					output_type="numpy" ).images

										lowerCamelCase__   =image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__   =np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3] )

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






				def 						_a      ( self ):
										lowerCamelCase__   ="google/ddpm-ema-bedroom-256"

										lowerCamelCase__   =UNetaDModel.from_pretrained(lowercase_ )
										lowerCamelCase__   =DDIMScheduler.from_pretrained(lowercase_ )

										lowerCamelCase__   =DDIMPipeline(unet=lowercase_      ,					scheduler=lowercase_ )
										ddpm.to(lowercase_ )
										ddpm.set_progress_bar_config(disable=lowercase_ )

										lowerCamelCase__   =torch.manual_seed(0 )
										lowerCamelCase__   =ddpm(generator=lowercase_      ,					output_type="numpy" ).images

										lowerCamelCase__   =image[0, -3:, -3:, -1]

										assert image.shape == (1, 256, 256, 3)
										lowerCamelCase__   =np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9] )

										assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''DebertaTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DebertaForMaskedLM''',
       '''DebertaForQuestionAnswering''',
       '''DebertaForSequenceClassification''',
       '''DebertaForTokenClassification''',
       '''DebertaModel''',
       '''DebertaPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFDebertaForMaskedLM''',
       '''TFDebertaForQuestionAnswering''',
       '''TFDebertaForSequenceClassification''',
       '''TFDebertaForTokenClassification''',
       '''TFDebertaModel''',
       '''TFDebertaPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




